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Theory and Methods

Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model

Pages 394-406 | Received 01 Sep 2013, Published online: 05 May 2016

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Xiao Guo & Hai Zhang. (2018) Sparse directed acyclic graphs incorporating the covariates. Statistical Papers 61:5, pages 2119-2148.
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Shanghong Xie, Xiang Li, Peter McColgan, Rachael I. Scahill, Donglin Zeng & Yuanjia Wang. (2019) Identifying disease‐associated biomarker network features through conditional graphical model. Biometrics 76:3, pages 995-1006.
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Yuehan Yang & Ji Zhu. (2020) A two-step method for estimating high-dimensional Gaussian graphical models. Science China Mathematics 63:6, pages 1203-1218.
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Matey Neykov, Junwei Lu & Han Liu. (2019) Combinatorial inference for graphical models. The Annals of Statistics 47:2.
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Rina Foygel Barber & Mladen Kolar. (2018) ROCKET: Robust confidence intervals via Kendall’s tau for transelliptical graphical models. The Annals of Statistics 46:6B.
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Adel Javanmard & Andrea Montanari. (2018) Debiasing the lasso: Optimal sample size for Gaussian designs. The Annals of Statistics 46:6A.
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Feihu Huang & Songcan Chen. (2018) Learning Dynamic Conditional Gaussian Graphical Models. IEEE Transactions on Knowledge and Data Engineering 30:4, pages 703-716.
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Guido Consonni, Luca La Rocca & Stefano Peluso. (2017) Objective Bayes Covariate‐Adjusted Sparse Graphical Model Selection. Scandinavian Journal of Statistics 44:3, pages 741-764.
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